Date of Award
Spring 1994
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Brown, Ronald H.
Second Advisor
Heinen, James A.
Third Advisor
Feng, Xin
Abstract
This thesis is concerned with the incorporation of a priori knowledge into feedforward artificial neural networks (ANNs). In most applications, an ANN is employed as a black box identification model, but the incorporation of any known information about the desired nonlinear mapping can improve identification. Various methods of incorporating a priori knowledge into ANNs have been proposed and one approach, the gray layer technique [5-9,29], is the focus of this thesis. The gray layer technique is examined in application to the identification of dynamic nonlinear systems. The technique is specifically applied to the identification of switched reluctance motor (SRM) state equations. The gray layer technique is compared to the conventional ANN architecture for both speed of convergence to the ANN estimate and on the basis of the ANNs ability to effectively identify the desired nonlinearity.
Recommended Citation
Behun, Bryan S., "Incorporating a Priori Knowledge into Artificial Neural Networks with Applications to Switched Reluctance Motors" (1994). Master's Theses (1922-2009) Access restricted to Marquette Campus. 3960.
https://epublications.marquette.edu/theses/3960